2006-11-04
 -----

Maven Settings Model

 This is the model for Maven settings in <<<org.apache.maven.settings>>> package,
 delegating content to {{{../../api/maven-api-settings/index.html}Maven 4 API immutable settings}}. All the effective model
 building logic from multiple settings files is done in {{{../maven-settings-builder/}Maven Settings Builder}}.

 The following are generated from this model:

 * specific language governing permissions and limitations
 * under the License.
 */
package org.apache.maven.model.building;

import java.util.Objects;

import org.apache.maven.model.InputLocation;
import org.apache.maven.model.building.ModelProblem.Severity;
import org.apache.maven.model.building.ModelProblem.Version;

/**
 * Class to wrap request parameters to ModelProblemCollector.addProblem
 *

    get_flat_models_from_fields,
    get_model_name_map,
    get_schema_from_model_field,
    lenient_issubclass,
)
from fastapi.datastructures import DefaultPlaceholder, _Unset
from fastapi.dependencies.models import Dependant
from fastapi.dependencies.utils import (
    _get_flat_fields_from_params,
    get_flat_dependant,
    get_flat_params,
    get_validation_alias,
)
from fastapi.encoders import jsonable_encoder

Dann verwenden wir den Request direkt und extrahieren den Body als `bytes`. Das bedeutet, dass FastAPI nicht einmal versucht, die Request-Payload als JSON zu parsen.

Und dann parsen wir in unserem Code diesen YAML-Inhalt direkt und verwenden dann wieder dasselbe Pydantic-Modell, um den YAML-Inhalt zu validieren:

        Item(name="baz", date=datetime(2021, 7, 26), price=2.0, owner_ids=[1, 2, 3]),
    ]


@app.get("/items/no-response-model/object")
def get_no_response_model_object():
    return Item(
        name="object", date=datetime(2021, 7, 26), price=1.0, owner_ids=[1, 2, 3]
    )


@app.get("/items/no-response-model/objectlist")
def get_no_response_model_objectlist():
    return [
        Item(name="foo", date=datetime(2021, 7, 26)),

    a: str | None = "foo"


class Model(BaseModel):
    x: int | None = None
    sub: SubModel


class ModelSubclass(Model):
    y: int
    z: int = 0
    w: int | None = None


class ModelDefaults(BaseModel):
    w: str | None = None
    x: str | None = None
    y: str = "y"
    z: str = "z"


@app.get("/", response_model=Model, response_model_exclude_unset=True)

		{Name: "ID", DBName: "id", BindNames: []string{"Model", "ID"}, DataType: schema.Uint, PrimaryKey: true, Tag: `gorm:"primarykey"`, TagSettings: map[string]string{"PRIMARYKEY": "PRIMARYKEY"}, Size: 64, HasDefaultValue: true, AutoIncrement: true},
		{Name: "CreatedAt", DBName: "created_at", BindNames: []string{"Model", "CreatedAt"}, DataType: schema.Time},
		{Name: "UpdatedAt", DBName: "updated_at", BindNames: []string{"Model", "UpdatedAt"}, DataType: schema.Time},

</div>

## 建立只有單一模型的應用 { #create-the-app-with-a-single-model }

我們先用單一 SQLModel 模型建立這個應用的最簡版。

接著我們會在下方用多個模型來提升安全性與彈性。🤓

### 建立模型 { #create-models }

匯入 `SQLModel` 並建立一個資料庫模型：

{* ../../docs_src/sql_databases/tutorial001_an_py310.py ln[1:11] hl[7:11] *}

`Hero` 類別與 Pydantic 模型非常相似（事實上，在底層它就是一個 Pydantic 模型）。

有幾點差異：

* `table=True` 告訴 SQLModel 這是一個「資料表模型」（table model），它應該代表 SQL 資料庫中的一個資料表，而不僅僅是「資料模型」（就像一般的 Pydantic 類別）。

import gradlebuild.binarycompatibility.jApiClass
import gradlebuild.binarycompatibility.simpleName
import japicmp.model.JApiClass
import japicmp.model.JApiCompatibility
import japicmp.model.JApiConstructor
import japicmp.model.JApiField
import japicmp.model.JApiMethod


private
typealias PredicateVisitor = GenericVisitorAdapter<Boolean, Unit?>

private

              <!--              <exclude>org.apache.maven.toolchain.merge.MavenToolchainMerger#mergeToolchainModelConfiguration(org.apache.maven.toolchain.model.ToolchainModel,org.apache.maven.toolchain.model.ToolchainModel):METHOD_REMOVED</exclude>-->
            </excludes>
          </parameter>